Overview

Brought to you by YData

Dataset statistics

Number of variables23
Number of observations7,566
Missing cells11,580
Missing cells (%)6.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.6 MiB
Average record size in memory1.0 KiB

Variable types

Text10
Numeric5
DateTime1
Categorical6
Boolean1

Alerts

IMD_Decile is highly overall correlated with IMD_RankHigh correlation
IMD_Rank is highly overall correlated with IMD_DecileHigh correlation
county is highly overall correlated with district and 1 other fieldsHigh correlation
current_energy_rating is highly overall correlated with new_buildHigh correlation
district is highly overall correlated with county and 1 other fieldsHigh correlation
ladnm is highly overall correlated with county and 1 other fieldsHigh correlation
new_build is highly overall correlated with current_energy_ratingHigh correlation
price is highly overall correlated with total_floor_areaHigh correlation
property_type is highly overall correlated with tenureHigh correlation
tenure is highly overall correlated with property_typeHigh correlation
total_floor_area is highly overall correlated with priceHigh correlation
new_build is highly imbalanced (71.5%)Imbalance
SAON has 7138 (94.3%) missing valuesMissing
Locality has 4404 (58.2%) missing valuesMissing
price is highly skewed (γ1 = 62.25331961)Skewed
transaction has unique valuesUnique
addr_key has unique valuesUnique

Reproduction

Analysis started2025-10-09 09:38:48.636909
Analysis finished2025-10-09 09:38:54.183030
Duration5.55 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

transaction
Text

Unique 

Distinct7566
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size960.0 KiB
2025-10-09T10:38:54.319093image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/

Length

Max length38
Median length38
Mean length38
Min length38

Characters and Unicode

Total characters287,508
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7,566 ?
Unique (%)100.0%

Sample

1st row{3DCCB7C9-D19E-5B9D-E063-4704A8C0331E}
2nd row{3DCCB7CA-8C58-5B9D-E063-4704A8C0331E}
3rd row{3DCCB7CA-8C30-5B9D-E063-4704A8C0331E}
4th row{3DCCB7CA-8CCC-5B9D-E063-4704A8C0331E}
5th row{3DCCB7C9-D364-5B9D-E063-4704A8C0331E}
ValueCountFrequency (%)
3dccb7c9-d19e-5b9d-e063-4704a8c0331e1
 
< 0.1%
3dccb7ca-85c8-5b9d-e063-4704a8c0331e1
 
< 0.1%
3dccb7c9-d364-5b9d-e063-4704a8c0331e1
 
< 0.1%
3dccb7ca-8d25-5b9d-e063-4704a8c0331e1
 
< 0.1%
3dccb7ca-1cf3-5b9d-e063-4704a8c0331e1
 
< 0.1%
3dccb7ca-8aef-5b9d-e063-4704a8c0331e1
 
< 0.1%
3dccb7ca-8e32-5b9d-e063-4704a8c0331e1
 
< 0.1%
3dccb7ca-4bec-5b9d-e063-4704a8c0331e1
 
< 0.1%
3dccb7ca-1ddc-5b9d-e063-4704a8c0331e1
 
< 0.1%
3dccb7c9-d317-5b9d-e063-4704a8c0331e1
 
< 0.1%
Other values (7556)7556
99.9%
2025-10-09T10:38:54.599922image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
C31916
11.1%
331869
11.1%
-30264
10.5%
024352
 
8.5%
D18068
 
6.3%
417758
 
6.2%
E16714
 
5.8%
B16605
 
5.8%
716273
 
5.7%
A15110
 
5.3%
Other values (9)68579
23.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)287508
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
C31916
11.1%
331869
11.1%
-30264
10.5%
024352
 
8.5%
D18068
 
6.3%
417758
 
6.2%
E16714
 
5.8%
B16605
 
5.8%
716273
 
5.7%
A15110
 
5.3%
Other values (9)68579
23.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)287508
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
C31916
11.1%
331869
11.1%
-30264
10.5%
024352
 
8.5%
D18068
 
6.3%
417758
 
6.2%
E16714
 
5.8%
B16605
 
5.8%
716273
 
5.7%
A15110
 
5.3%
Other values (9)68579
23.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)287508
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
C31916
11.1%
331869
11.1%
-30264
10.5%
024352
 
8.5%
D18068
 
6.3%
417758
 
6.2%
E16714
 
5.8%
B16605
 
5.8%
716273
 
5.7%
A15110
 
5.3%
Other values (9)68579
23.9%

price
Real number (ℝ)

High correlation  Skewed 

Distinct1192
Distinct (%)15.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean298859.48
Minimum10000
Maximum31000000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size376.3 KiB
2025-10-09T10:38:54.878919image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/

Quantile statistics

Minimum10000
5-th percentile115000
Q1190000
median254999.5
Q3349995
95-th percentile610000
Maximum31000000
Range30990000
Interquartile range (IQR)159995

Descriptive statistics

Standard deviation395259.29
Coefficient of variation (CV)1.322559
Kurtosis4813.3379
Mean298859.48
Median Absolute Deviation (MAD)74999.5
Skewness62.25332
Sum2.2611708 × 109
Variance1.562299 × 1011
MonotonicityNot monotonic
2025-10-09T10:38:55.004138image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
250000130
 
1.7%
200000123
 
1.6%
220000117
 
1.5%
180000111
 
1.5%
230000105
 
1.4%
21000097
 
1.3%
24000094
 
1.2%
21500094
 
1.2%
19000094
 
1.2%
22500091
 
1.2%
Other values (1182)6510
86.0%
ValueCountFrequency (%)
100001
 
< 0.1%
148201
 
< 0.1%
150001
 
< 0.1%
200001
 
< 0.1%
250001
 
< 0.1%
325001
 
< 0.1%
350003
< 0.1%
395651
 
< 0.1%
396001
 
< 0.1%
400001
 
< 0.1%
ValueCountFrequency (%)
310000001
< 0.1%
28000001
< 0.1%
27000001
< 0.1%
21750001
< 0.1%
20000001
< 0.1%
19000001
< 0.1%
18375001
< 0.1%
18000001
< 0.1%
17000001
< 0.1%
16750001
< 0.1%
Distinct488
Distinct (%)6.4%
Missing0
Missing (%)0.0%
Memory size376.3 KiB
Minimum1996-11-29 00:00:00
Maximum2025-08-29 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-10-09T10:38:55.135189image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
2025-10-09T10:38:55.287179image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct7030
Distinct (%)92.9%
Missing0
Missing (%)0.0%
Memory size733.9 KiB
2025-10-09T10:38:55.648267image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/

Length

Max length8
Median length7
Mean length7.4039122
Min length6

Characters and Unicode

Total characters56,018
Distinct characters35
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6,617 ?
Unique (%)87.5%

Sample

1st rowS18 1QH
2nd rowCV4 7PA
3rd rowB44 0JR
4th rowDY4 7NY
5th rowDE7 6GU
ValueCountFrequency (%)
le67106
 
0.7%
st5102
 
0.7%
cv691
 
0.6%
le1289
 
0.6%
le988
 
0.6%
cv3787
 
0.6%
le286
 
0.6%
le1081
 
0.5%
le478
 
0.5%
le776
 
0.5%
Other values (3209)14248
94.2%
2025-10-09T10:38:56.085614image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
7566
 
13.5%
13822
 
6.8%
E3036
 
5.4%
B2625
 
4.7%
32439
 
4.4%
22356
 
4.2%
D2290
 
4.1%
S2272
 
4.1%
42112
 
3.8%
L2078
 
3.7%
Other values (25)25422
45.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)56018
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
7566
 
13.5%
13822
 
6.8%
E3036
 
5.4%
B2625
 
4.7%
32439
 
4.4%
22356
 
4.2%
D2290
 
4.1%
S2272
 
4.1%
42112
 
3.8%
L2078
 
3.7%
Other values (25)25422
45.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)56018
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
7566
 
13.5%
13822
 
6.8%
E3036
 
5.4%
B2625
 
4.7%
32439
 
4.4%
22356
 
4.2%
D2290
 
4.1%
S2272
 
4.1%
42112
 
3.8%
L2078
 
3.7%
Other values (25)25422
45.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)56018
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
7566
 
13.5%
13822
 
6.8%
E3036
 
5.4%
B2625
 
4.7%
32439
 
4.4%
22356
 
4.2%
D2290
 
4.1%
S2272
 
4.1%
42112
 
3.8%
L2078
 
3.7%
Other values (25)25422
45.4%

property_type
Categorical

High correlation 

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size686.6 KiB
S
2852 
D
2156 
T
1882 
F
676 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters7,566
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowD
2nd rowD
3rd rowS
4th rowS
5th rowD

Common Values

ValueCountFrequency (%)
S2852
37.7%
D2156
28.5%
T1882
24.9%
F676
 
8.9%

Length

2025-10-09T10:38:56.190225image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-09T10:38:56.267997image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
ValueCountFrequency (%)
s2852
37.7%
d2156
28.5%
t1882
24.9%
f676
 
8.9%

Most occurring characters

ValueCountFrequency (%)
S2852
37.7%
D2156
28.5%
T1882
24.9%
F676
 
8.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)7566
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S2852
37.7%
D2156
28.5%
T1882
24.9%
F676
 
8.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)7566
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S2852
37.7%
D2156
28.5%
T1882
24.9%
F676
 
8.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)7566
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S2852
37.7%
D2156
28.5%
T1882
24.9%
F676
 
8.9%

new_build
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size324.5 KiB
False
7191 
True
 
375
ValueCountFrequency (%)
False7191
95.0%
True375
 
5.0%
2025-10-09T10:38:56.323042image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/

tenure
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size686.6 KiB
F
6726 
L
840 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters7,566
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowF
2nd rowF
3rd rowF
4th rowF
5th rowF

Common Values

ValueCountFrequency (%)
F6726
88.9%
L840
 
11.1%

Length

2025-10-09T10:38:56.400455image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-09T10:38:56.470450image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
ValueCountFrequency (%)
f6726
88.9%
l840
 
11.1%

Most occurring characters

ValueCountFrequency (%)
F6726
88.9%
L840
 
11.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)7566
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
F6726
88.9%
L840
 
11.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)7566
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
F6726
88.9%
L840
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)7566
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
F6726
88.9%
L840
 
11.1%

PAON
Text

Distinct1035
Distinct (%)13.7%
Missing0
Missing (%)0.0%
Memory size700.6 KiB
2025-10-09T10:38:56.752508image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/

Length

Max length35
Median length2
Mean length2.9039122
Min length1

Characters and Unicode

Total characters21,971
Distinct characters41
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique729 ?
Unique (%)9.6%

Sample

1st row27
2nd row1
3rd row56
4th row22
5th row3
ValueCountFrequency (%)
1206
 
2.5%
4196
 
2.3%
8188
 
2.2%
5186
 
2.2%
2182
 
2.2%
7178
 
2.1%
6172
 
2.1%
3171
 
2.0%
12164
 
2.0%
9161
 
1.9%
Other values (1042)6573
78.5%
2025-10-09T10:38:57.212887image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
12829
 
12.9%
21941
 
8.8%
31532
 
7.0%
41354
 
6.2%
51219
 
5.5%
61037
 
4.7%
7936
 
4.3%
8907
 
4.1%
E893
 
4.1%
9833
 
3.8%
Other values (31)8490
38.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)21971
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
12829
 
12.9%
21941
 
8.8%
31532
 
7.0%
41354
 
6.2%
51219
 
5.5%
61037
 
4.7%
7936
 
4.3%
8907
 
4.1%
E893
 
4.1%
9833
 
3.8%
Other values (31)8490
38.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)21971
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
12829
 
12.9%
21941
 
8.8%
31532
 
7.0%
41354
 
6.2%
51219
 
5.5%
61037
 
4.7%
7936
 
4.3%
8907
 
4.1%
E893
 
4.1%
9833
 
3.8%
Other values (31)8490
38.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)21971
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
12829
 
12.9%
21941
 
8.8%
31532
 
7.0%
41354
 
6.2%
51219
 
5.5%
61037
 
4.7%
7936
 
4.3%
8907
 
4.1%
E893
 
4.1%
9833
 
3.8%
Other values (31)8490
38.6%

SAON
Text

Missing 

Distinct177
Distinct (%)41.4%
Missing7138
Missing (%)94.3%
Memory size563.6 KiB
2025-10-09T10:38:57.423345image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/

Length

Max length27
Median length19
Mean length6.8668224
Min length1

Characters and Unicode

Total characters2,939
Distinct characters32
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique108 ?
Unique (%)25.2%

Sample

1st rowAPARTMENT 1
2nd row26
3rd row306
4th rowAPARTMENT 9
5th row1
ValueCountFrequency (%)
flat192
25.5%
apartment110
14.6%
143
 
5.7%
328
 
3.7%
227
 
3.6%
424
 
3.2%
519
 
2.5%
718
 
2.4%
617
 
2.3%
814
 
1.9%
Other values (107)260
34.6%
2025-10-09T10:38:57.747143image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
T432
14.7%
A427
14.5%
324
11.0%
L201
 
6.8%
F194
 
6.6%
1174
 
5.9%
E138
 
4.7%
R123
 
4.2%
N116
 
3.9%
2114
 
3.9%
Other values (22)696
23.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)2939
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T432
14.7%
A427
14.5%
324
11.0%
L201
 
6.8%
F194
 
6.6%
1174
 
5.9%
E138
 
4.7%
R123
 
4.2%
N116
 
3.9%
2114
 
3.9%
Other values (22)696
23.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2939
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T432
14.7%
A427
14.5%
324
11.0%
L201
 
6.8%
F194
 
6.6%
1174
 
5.9%
E138
 
4.7%
R123
 
4.2%
N116
 
3.9%
2114
 
3.9%
Other values (22)696
23.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2939
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T432
14.7%
A427
14.5%
324
11.0%
L201
 
6.8%
F194
 
6.6%
1174
 
5.9%
E138
 
4.7%
R123
 
4.2%
N116
 
3.9%
2114
 
3.9%
Other values (22)696
23.7%

Street
Text

Distinct5682
Distinct (%)75.5%
Missing38
Missing (%)0.5%
Memory size775.7 KiB
2025-10-09T10:38:58.036463image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/

Length

Max length26
Median length23
Mean length13.212806
Min length4

Characters and Unicode

Total characters99,466
Distinct characters28
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4,652 ?
Unique (%)61.8%

Sample

1st rowHOLBEIN CLOSE
2nd rowTHE LAURELS
3rd rowHORNSEY ROAD
4th rowDARBYS WAY
5th rowBEECH LANE
ValueCountFrequency (%)
road2388
 
15.2%
close881
 
5.6%
street677
 
4.3%
drive614
 
3.9%
avenue586
 
3.7%
lane553
 
3.5%
way341
 
2.2%
crescent223
 
1.4%
grove173
 
1.1%
the126
 
0.8%
Other values (4129)9128
58.2%
2025-10-09T10:38:58.435796image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E12131
12.2%
R9296
 
9.3%
A8572
 
8.6%
8162
 
8.2%
O8076
 
8.1%
L5977
 
6.0%
D5634
 
5.7%
N5542
 
5.6%
S5256
 
5.3%
T5066
 
5.1%
Other values (18)25754
25.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)99466
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E12131
12.2%
R9296
 
9.3%
A8572
 
8.6%
8162
 
8.2%
O8076
 
8.1%
L5977
 
6.0%
D5634
 
5.7%
N5542
 
5.6%
S5256
 
5.3%
T5066
 
5.1%
Other values (18)25754
25.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)99466
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E12131
12.2%
R9296
 
9.3%
A8572
 
8.6%
8162
 
8.2%
O8076
 
8.1%
L5977
 
6.0%
D5634
 
5.7%
N5542
 
5.6%
S5256
 
5.3%
T5066
 
5.1%
Other values (18)25754
25.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)99466
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E12131
12.2%
R9296
 
9.3%
A8572
 
8.6%
8162
 
8.2%
O8076
 
8.1%
L5977
 
6.0%
D5634
 
5.7%
N5542
 
5.6%
S5256
 
5.3%
T5066
 
5.1%
Other values (18)25754
25.9%

Locality
Text

Missing 

Distinct844
Distinct (%)26.7%
Missing4404
Missing (%)58.2%
Memory size635.2 KiB
2025-10-09T10:38:58.689129image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/

Length

Max length22
Median length19
Mean length9.4408602
Min length3

Characters and Unicode

Total characters29,852
Distinct characters28
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique357 ?
Unique (%)11.3%

Sample

1st rowWEST HALLAM
2nd rowCHELLASTON
3rd rowNETHERTON
4th rowWELLESBOURNE
5th rowPINXTON
ValueCountFrequency (%)
shirley63
 
1.5%
long59
 
1.4%
green52
 
1.2%
eaton51
 
1.2%
heath49
 
1.2%
great40
 
1.0%
on39
 
0.9%
shepshed33
 
0.8%
mickleover31
 
0.7%
hill31
 
0.7%
Other values (860)3713
89.2%
2025-10-09T10:38:59.058822image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E3221
 
10.8%
O2803
 
9.4%
L2434
 
8.2%
N2264
 
7.6%
R2158
 
7.2%
T1928
 
6.5%
A1850
 
6.2%
S1678
 
5.6%
H1434
 
4.8%
I1307
 
4.4%
Other values (18)8775
29.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)29852
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E3221
 
10.8%
O2803
 
9.4%
L2434
 
8.2%
N2264
 
7.6%
R2158
 
7.2%
T1928
 
6.5%
A1850
 
6.2%
S1678
 
5.6%
H1434
 
4.8%
I1307
 
4.4%
Other values (18)8775
29.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)29852
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E3221
 
10.8%
O2803
 
9.4%
L2434
 
8.2%
N2264
 
7.6%
R2158
 
7.2%
T1928
 
6.5%
A1850
 
6.2%
S1678
 
5.6%
H1434
 
4.8%
I1307
 
4.4%
Other values (18)8775
29.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)29852
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E3221
 
10.8%
O2803
 
9.4%
L2434
 
8.2%
N2264
 
7.6%
R2158
 
7.2%
T1928
 
6.5%
A1850
 
6.2%
S1678
 
5.6%
H1434
 
4.8%
I1307
 
4.4%
Other values (18)8775
29.4%
Distinct94
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size750.9 KiB
2025-10-09T10:38:59.268791image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/

Length

Max length19
Median length15
Mean length9.704996
Min length4

Characters and Unicode

Total characters73,428
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowDRONFIELD
2nd rowCOVENTRY
3rd rowBIRMINGHAM
4th rowTIPTON
5th rowILKESTON
ValueCountFrequency (%)
birmingham789
 
9.7%
leicester549
 
6.7%
stoke-on-trent370
 
4.5%
derby370
 
4.5%
coventry360
 
4.4%
wolverhampton256
 
3.1%
chesterfield218
 
2.7%
worcester214
 
2.6%
solihull173
 
2.1%
walsall169
 
2.1%
Other values (95)4683
57.5%
2025-10-09T10:38:59.618064image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E7969
 
10.9%
R6467
 
8.8%
T6037
 
8.2%
O5836
 
7.9%
N5073
 
6.9%
L4609
 
6.3%
I4473
 
6.1%
A3569
 
4.9%
S3428
 
4.7%
H3103
 
4.2%
Other values (16)22864
31.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)73428
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E7969
 
10.9%
R6467
 
8.8%
T6037
 
8.2%
O5836
 
7.9%
N5073
 
6.9%
L4609
 
6.3%
I4473
 
6.1%
A3569
 
4.9%
S3428
 
4.7%
H3103
 
4.2%
Other values (16)22864
31.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)73428
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E7969
 
10.9%
R6467
 
8.8%
T6037
 
8.2%
O5836
 
7.9%
N5073
 
6.9%
L4609
 
6.3%
I4473
 
6.1%
A3569
 
4.9%
S3428
 
4.7%
H3103
 
4.2%
Other values (16)22864
31.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)73428
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E7969
 
10.9%
R6467
 
8.8%
T6037
 
8.2%
O5836
 
7.9%
N5073
 
6.9%
L4609
 
6.3%
I4473
 
6.1%
A3569
 
4.9%
S3428
 
4.7%
H3103
 
4.2%
Other values (16)22864
31.1%

district
Categorical

High correlation 

Distinct44
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size764.8 KiB
BIRMINGHAM
763 
COVENTRY
 
317
DUDLEY
 
296
CITY OF DERBY
 
252
STOKE-ON-TRENT
 
242
Other values (39)
5696 

Length

Max length25
Median length20
Mean length11.588025
Min length5

Characters and Unicode

Total characters87,675
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNORTH EAST DERBYSHIRE
2nd rowCOVENTRY
3rd rowBIRMINGHAM
4th rowSANDWELL
5th rowEREWASH

Common Values

ValueCountFrequency (%)
BIRMINGHAM763
 
10.1%
COVENTRY317
 
4.2%
DUDLEY296
 
3.9%
CITY OF DERBY252
 
3.3%
STOKE-ON-TRENT242
 
3.2%
SANDWELL240
 
3.2%
SOLIHULL228
 
3.0%
CHARNWOOD228
 
3.0%
WARWICK225
 
3.0%
WALSALL218
 
2.9%
Other values (34)4557
60.2%

Length

2025-10-09T10:38:59.745042image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
birmingham763
 
7.1%
staffordshire397
 
3.7%
and396
 
3.7%
north353
 
3.3%
derbyshire321
 
3.0%
coventry317
 
3.0%
dudley296
 
2.8%
east272
 
2.5%
south254
 
2.4%
city252
 
2.3%
Other values (47)7116
66.3%

Most occurring characters

ValueCountFrequency (%)
E8218
 
9.4%
R7962
 
9.1%
O6452
 
7.4%
A5827
 
6.6%
T5386
 
6.1%
S5272
 
6.0%
N5077
 
5.8%
H5035
 
5.7%
L4757
 
5.4%
I4614
 
5.3%
Other values (14)29075
33.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)87675
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E8218
 
9.4%
R7962
 
9.1%
O6452
 
7.4%
A5827
 
6.6%
T5386
 
6.1%
S5272
 
6.0%
N5077
 
5.8%
H5035
 
5.7%
L4757
 
5.4%
I4614
 
5.3%
Other values (14)29075
33.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)87675
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E8218
 
9.4%
R7962
 
9.1%
O6452
 
7.4%
A5827
 
6.6%
T5386
 
6.1%
S5272
 
6.0%
N5077
 
5.8%
H5035
 
5.7%
L4757
 
5.4%
I4614
 
5.3%
Other values (14)29075
33.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)87675
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E8218
 
9.4%
R7962
 
9.1%
O6452
 
7.4%
A5827
 
6.6%
T5386
 
6.1%
S5272
 
6.0%
N5077
 
5.8%
H5035
 
5.7%
L4757
 
5.4%
I4614
 
5.3%
Other values (14)29075
33.2%

county
Categorical

High correlation 

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size772.8 KiB
WEST MIDLANDS
2276 
STAFFORDSHIRE
1056 
LEICESTERSHIRE
1021 
DERBYSHIRE
980 
WARWICKSHIRE
804 
Other values (4)
1429 

Length

Max length14
Median length13
Mean length12.666799
Min length9

Characters and Unicode

Total characters95,837
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDERBYSHIRE
2nd rowWEST MIDLANDS
3rd rowWEST MIDLANDS
4th rowWEST MIDLANDS
5th rowDERBYSHIRE

Common Values

ValueCountFrequency (%)
WEST MIDLANDS2276
30.1%
STAFFORDSHIRE1056
14.0%
LEICESTERSHIRE1021
13.5%
DERBYSHIRE980
13.0%
WARWICKSHIRE804
 
10.6%
WORCESTERSHIRE740
 
9.8%
CITY OF DERBY252
 
3.3%
STOKE-ON-TRENT242
 
3.2%
LEICESTER195
 
2.6%

Length

2025-10-09T10:38:59.857062image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-09T10:38:59.950085image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
ValueCountFrequency (%)
west2276
22.0%
midlands2276
22.0%
staffordshire1056
10.2%
leicestershire1021
9.9%
derbyshire980
9.5%
warwickshire804
 
7.8%
worcestershire740
 
7.2%
city252
 
2.4%
of252
 
2.4%
derby252
 
2.4%
Other values (2)437
 
4.2%

Most occurring characters

ValueCountFrequency (%)
E13721
14.3%
S12407
12.9%
R10631
11.1%
I9149
9.5%
D6840
 
7.1%
T6266
 
6.5%
W4624
 
4.8%
H4601
 
4.8%
A4136
 
4.3%
L3492
 
3.6%
Other values (10)19970
20.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)95837
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E13721
14.3%
S12407
12.9%
R10631
11.1%
I9149
9.5%
D6840
 
7.1%
T6266
 
6.5%
W4624
 
4.8%
H4601
 
4.8%
A4136
 
4.3%
L3492
 
3.6%
Other values (10)19970
20.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)95837
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E13721
14.3%
S12407
12.9%
R10631
11.1%
I9149
9.5%
D6840
 
7.1%
T6266
 
6.5%
W4624
 
4.8%
H4601
 
4.8%
A4136
 
4.3%
L3492
 
3.6%
Other values (10)19970
20.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)95837
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E13721
14.3%
S12407
12.9%
R10631
11.1%
I9149
9.5%
D6840
 
7.1%
T6266
 
6.5%
W4624
 
4.8%
H4601
 
4.8%
A4136
 
4.3%
L3492
 
3.6%
Other values (10)19970
20.8%
Distinct3296
Distinct (%)43.6%
Missing0
Missing (%)0.0%
Memory size745.7 KiB
2025-10-09T10:39:00.242925image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters68,094
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1,278 ?
Unique (%)16.9%

Sample

1st rowE01019793
2nd rowE01009665
3rd rowE01009128
4th rowE01009976
5th rowE01019703
ValueCountFrequency (%)
e0103123124
 
0.3%
e0103289221
 
0.3%
e0102578916
 
0.2%
e0102591713
 
0.2%
e0102983613
 
0.2%
e0100910213
 
0.2%
e0102595713
 
0.2%
e0102941612
 
0.2%
e0102570312
 
0.2%
e0101022312
 
0.2%
Other values (3286)7417
98.0%
2025-10-09T10:39:00.604936image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
019177
28.2%
112860
18.9%
E7566
 
11.1%
25404
 
7.9%
95317
 
7.8%
34531
 
6.7%
53425
 
5.0%
42679
 
3.9%
82415
 
3.5%
72360
 
3.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)68094
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
019177
28.2%
112860
18.9%
E7566
 
11.1%
25404
 
7.9%
95317
 
7.8%
34531
 
6.7%
53425
 
5.0%
42679
 
3.9%
82415
 
3.5%
72360
 
3.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)68094
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
019177
28.2%
112860
18.9%
E7566
 
11.1%
25404
 
7.9%
95317
 
7.8%
34531
 
6.7%
53425
 
5.0%
42679
 
3.9%
82415
 
3.5%
72360
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)68094
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
019177
28.2%
112860
18.9%
E7566
 
11.1%
25404
 
7.9%
95317
 
7.8%
34531
 
6.7%
53425
 
5.0%
42679
 
3.9%
82415
 
3.5%
72360
 
3.5%
Distinct892
Distinct (%)11.8%
Missing0
Missing (%)0.0%
Memory size792.4 KiB
2025-10-09T10:39:00.832911image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/

Length

Max length29
Median length24
Mean length15.317076
Min length9

Characters and Unicode

Total characters115,889
Distinct characters51
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique23 ?
Unique (%)0.3%

Sample

1st rowNorth East Derbyshire 006
2nd rowCoventry 042
3rd rowBirmingham 011
4th rowSandwell 016
5th rowErewash 005
ValueCountFrequency (%)
birmingham764
 
4.3%
001432
 
2.4%
003427
 
2.4%
006416
 
2.3%
005413
 
2.3%
010412
 
2.3%
staffordshire397
 
2.2%
and395
 
2.2%
009391
 
2.2%
008387
 
2.2%
Other values (172)13363
75.1%
2025-10-09T10:39:01.182380image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
011545
 
10.0%
10231
 
8.8%
e7794
 
6.7%
r7705
 
6.6%
o6121
 
5.3%
a5456
 
4.7%
t4780
 
4.1%
h4473
 
3.9%
n4419
 
3.8%
i4359
 
3.8%
Other values (41)49006
42.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)115889
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
011545
 
10.0%
10231
 
8.8%
e7794
 
6.7%
r7705
 
6.6%
o6121
 
5.3%
a5456
 
4.7%
t4780
 
4.1%
h4473
 
3.9%
n4419
 
3.8%
i4359
 
3.8%
Other values (41)49006
42.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)115889
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
011545
 
10.0%
10231
 
8.8%
e7794
 
6.7%
r7705
 
6.6%
o6121
 
5.3%
a5456
 
4.7%
t4780
 
4.1%
h4473
 
3.9%
n4419
 
3.8%
i4359
 
3.8%
Other values (41)49006
42.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)115889
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
011545
 
10.0%
10231
 
8.8%
e7794
 
6.7%
r7705
 
6.6%
o6121
 
5.3%
a5456
 
4.7%
t4780
 
4.1%
h4473
 
3.9%
n4419
 
3.8%
i4359
 
3.8%
Other values (41)49006
42.3%

ladnm
Categorical

High correlation 

Distinct44
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size762.8 KiB
Birmingham
764 
Coventry
 
316
Dudley
 
297
Derby
 
252
Stoke-on-Trent
 
240
Other values (39)
5697 

Length

Max length25
Median length20
Mean length11.317076
Min length5

Characters and Unicode

Total characters85,625
Distinct characters41
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNorth East Derbyshire
2nd rowCoventry
3rd rowBirmingham
4th rowSandwell
5th rowErewash

Common Values

ValueCountFrequency (%)
Birmingham764
 
10.1%
Coventry316
 
4.2%
Dudley297
 
3.9%
Derby252
 
3.3%
Stoke-on-Trent240
 
3.2%
Sandwell238
 
3.1%
Charnwood230
 
3.0%
Solihull228
 
3.0%
Warwick225
 
3.0%
Walsall219
 
2.9%
Other values (34)4557
60.2%

Length

2025-10-09T10:39:01.303463image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
birmingham764
 
7.5%
staffordshire397
 
3.9%
and395
 
3.9%
north353
 
3.5%
derbyshire321
 
3.1%
coventry316
 
3.1%
dudley297
 
2.9%
east274
 
2.7%
south253
 
2.5%
derby252
 
2.5%
Other values (45)6609
64.6%

Most occurring characters

ValueCountFrequency (%)
e7794
 
9.1%
r7705
 
9.0%
o6121
 
7.1%
a5456
 
6.4%
t4780
 
5.6%
h4473
 
5.2%
n4419
 
5.2%
i4359
 
5.1%
l4086
 
4.8%
s3544
 
4.1%
Other values (31)32888
38.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)85625
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e7794
 
9.1%
r7705
 
9.0%
o6121
 
7.1%
a5456
 
6.4%
t4780
 
5.6%
h4473
 
5.2%
n4419
 
5.2%
i4359
 
5.1%
l4086
 
4.8%
s3544
 
4.1%
Other values (31)32888
38.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)85625
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e7794
 
9.1%
r7705
 
9.0%
o6121
 
7.1%
a5456
 
6.4%
t4780
 
5.6%
h4473
 
5.2%
n4419
 
5.2%
i4359
 
5.1%
l4086
 
4.8%
s3544
 
4.1%
Other values (31)32888
38.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)85625
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e7794
 
9.1%
r7705
 
9.0%
o6121
 
7.1%
a5456
 
6.4%
t4780
 
5.6%
h4473
 
5.2%
n4419
 
5.2%
i4359
 
5.1%
l4086
 
4.8%
s3544
 
4.1%
Other values (31)32888
38.4%

IMD_Decile
Real number (ℝ)

High correlation 

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.9413164
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size376.3 KiB
2025-10-09T10:39:01.405751image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median6
Q38
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.8111746
Coefficient of variation (CV)0.47315685
Kurtosis-1.149525
Mean5.9413164
Median Absolute Deviation (MAD)2
Skewness-0.21349687
Sum44952
Variance7.9027025
MonotonicityNot monotonic
2025-10-09T10:39:01.483528image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
8995
13.2%
9924
12.2%
10841
11.1%
7778
10.3%
5757
10.0%
6756
10.0%
4708
9.4%
3628
8.3%
2604
8.0%
1575
7.6%
ValueCountFrequency (%)
1575
7.6%
2604
8.0%
3628
8.3%
4708
9.4%
5757
10.0%
6756
10.0%
7778
10.3%
8995
13.2%
9924
12.2%
10841
11.1%
ValueCountFrequency (%)
10841
11.1%
9924
12.2%
8995
13.2%
7778
10.3%
6756
10.0%
5757
10.0%
4708
9.4%
3628
8.3%
2604
8.0%
1575
7.6%

IMD_Rank
Real number (ℝ)

High correlation 

Distinct3296
Distinct (%)43.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17889.175
Minimum146
Maximum32825
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size376.3 KiB
2025-10-09T10:39:01.601658image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/

Quantile statistics

Minimum146
5-th percentile2431
Q110273
median18664
Q325910
95-th percentile31270
Maximum32825
Range32679
Interquartile range (IQR)15637

Descriptive statistics

Standard deviation9231.5301
Coefficient of variation (CV)0.51604002
Kurtosis-1.1541936
Mean17889.175
Median Absolute Deviation (MAD)7774
Skewness-0.20239516
Sum1.353495 × 108
Variance85221149
MonotonicityNot monotonic
2025-10-09T10:39:01.724188image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1301424
 
0.3%
2151121
 
0.3%
1073316
 
0.2%
2800313
 
0.2%
1250613
 
0.2%
301913
 
0.2%
1365013
 
0.2%
1521712
 
0.2%
2299812
 
0.2%
1092612
 
0.2%
Other values (3286)7417
98.0%
ValueCountFrequency (%)
1461
 
< 0.1%
1781
 
< 0.1%
2021
 
< 0.1%
2032
 
< 0.1%
2061
 
< 0.1%
2251
 
< 0.1%
2681
 
< 0.1%
2953
< 0.1%
3401
 
< 0.1%
3525
0.1%
ValueCountFrequency (%)
328251
 
< 0.1%
328043
< 0.1%
327764
0.1%
327742
 
< 0.1%
327643
< 0.1%
327232
 
< 0.1%
327186
0.1%
326901
 
< 0.1%
326771
 
< 0.1%
326503
< 0.1%

addr_key
Text

Unique 

Distinct7566
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size870.4 KiB
2025-10-09T10:39:02.053737image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/

Length

Max length66
Median length60
Mean length25.871927
Min length10

Characters and Unicode

Total characters195,747
Distinct characters37
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7,566 ?
Unique (%)100.0%

Sample

1st row27 holbein close s18 1qh
2nd row1 the laurels cv4 7pa
3rd row56 hornsey road b44 0jr
4th row22 darbys way dy4 7ny
5th row3 beech lane de7 6gu
ValueCountFrequency (%)
road2390
 
6.0%
close881
 
2.2%
street677
 
1.7%
drive614
 
1.5%
avenue586
 
1.5%
lane553
 
1.4%
way342
 
0.9%
1249
 
0.6%
crescent223
 
0.6%
4220
 
0.6%
Other values (8100)33212
83.1%
2025-10-09T10:39:02.743811image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
32381
16.5%
e16198
 
8.3%
r11162
 
5.7%
a10583
 
5.4%
o8841
 
4.5%
l8707
 
4.4%
d8188
 
4.2%
s8000
 
4.1%
t7482
 
3.8%
n7023
 
3.6%
Other values (27)77182
39.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)195747
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
32381
16.5%
e16198
 
8.3%
r11162
 
5.7%
a10583
 
5.4%
o8841
 
4.5%
l8707
 
4.4%
d8188
 
4.2%
s8000
 
4.1%
t7482
 
3.8%
n7023
 
3.6%
Other values (27)77182
39.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)195747
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
32381
16.5%
e16198
 
8.3%
r11162
 
5.7%
a10583
 
5.4%
o8841
 
4.5%
l8707
 
4.4%
d8188
 
4.2%
s8000
 
4.1%
t7482
 
3.8%
n7023
 
3.6%
Other values (27)77182
39.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)195747
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
32381
16.5%
e16198
 
8.3%
r11162
 
5.7%
a10583
 
5.4%
o8841
 
4.5%
l8707
 
4.4%
d8188
 
4.2%
s8000
 
4.1%
t7482
 
3.8%
n7023
 
3.6%
Other values (27)77182
39.4%

current_energy_rating
Categorical

High correlation 

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size686.6 KiB
D
3314 
C
1882 
E
1043 
B
1008 
F
 
212
Other values (2)
 
107

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters7,566
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowD
2nd rowC
3rd rowE
4th rowD
5th rowD

Common Values

ValueCountFrequency (%)
D3314
43.8%
C1882
24.9%
E1043
 
13.8%
B1008
 
13.3%
F212
 
2.8%
A70
 
0.9%
G37
 
0.5%

Length

2025-10-09T10:39:02.864158image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-09T10:39:02.946757image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
ValueCountFrequency (%)
d3314
43.8%
c1882
24.9%
e1043
 
13.8%
b1008
 
13.3%
f212
 
2.8%
a70
 
0.9%
g37
 
0.5%

Most occurring characters

ValueCountFrequency (%)
D3314
43.8%
C1882
24.9%
E1043
 
13.8%
B1008
 
13.3%
F212
 
2.8%
A70
 
0.9%
G37
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)7566
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
D3314
43.8%
C1882
24.9%
E1043
 
13.8%
B1008
 
13.3%
F212
 
2.8%
A70
 
0.9%
G37
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)7566
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
D3314
43.8%
C1882
24.9%
E1043
 
13.8%
B1008
 
13.3%
F212
 
2.8%
A70
 
0.9%
G37
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)7566
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
D3314
43.8%
C1882
24.9%
E1043
 
13.8%
B1008
 
13.3%
F212
 
2.8%
A70
 
0.9%
G37
 
0.5%

total_floor_area
Real number (ℝ)

High correlation 

Distinct1114
Distinct (%)14.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean95.380055
Minimum0
Maximum1345
Zeros4
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size376.3 KiB
2025-10-09T10:39:03.069966image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile52
Q171.51
median85.83
Q3107
95-th percentile170
Maximum1345
Range1345
Interquartile range (IQR)35.49

Descriptive statistics

Standard deviation44.282204
Coefficient of variation (CV)0.46427112
Kurtosis96.436589
Mean95.380055
Median Absolute Deviation (MAD)16.83
Skewness5.430991
Sum721645.49
Variance1960.9136
MonotonicityNot monotonic
2025-10-09T10:39:03.213528image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
79147
 
1.9%
80140
 
1.9%
83139
 
1.8%
86134
 
1.8%
77129
 
1.7%
74127
 
1.7%
78123
 
1.6%
84122
 
1.6%
82119
 
1.6%
81116
 
1.5%
Other values (1104)6270
82.9%
ValueCountFrequency (%)
04
0.1%
51
 
< 0.1%
121
 
< 0.1%
171
 
< 0.1%
191
 
< 0.1%
261
 
< 0.1%
271
 
< 0.1%
281
 
< 0.1%
301
 
< 0.1%
30.91
 
< 0.1%
ValueCountFrequency (%)
13451
< 0.1%
6341
< 0.1%
5251
< 0.1%
4951
< 0.1%
4771
< 0.1%
4751
< 0.1%
455.851
< 0.1%
4311
< 0.1%
4131
< 0.1%
4121
< 0.1%

year
Real number (ℝ)

Distinct13
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2024.733
Minimum1996
Maximum2025
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size346.7 KiB
2025-10-09T10:39:03.326607image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/

Quantile statistics

Minimum1996
5-th percentile2024
Q12025
median2025
Q32025
95-th percentile2025
Maximum2025
Range29
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.90242331
Coefficient of variation (CV)0.0004456999
Kurtosis327.1455
Mean2024.733
Median Absolute Deviation (MAD)0
Skewness-14.241169
Sum15319130
Variance0.81436782
MonotonicityIncreasing
2025-10-09T10:39:03.428604image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
20256059
80.1%
20241334
 
17.6%
202364
 
0.8%
202249
 
0.6%
202134
 
0.4%
20209
 
0.1%
20156
 
0.1%
20194
 
0.1%
20052
 
< 0.1%
20082
 
< 0.1%
Other values (3)3
 
< 0.1%
ValueCountFrequency (%)
19961
 
< 0.1%
20011
 
< 0.1%
20052
 
< 0.1%
20061
 
< 0.1%
20082
 
< 0.1%
20156
 
0.1%
20194
 
0.1%
20209
 
0.1%
202134
0.4%
202249
0.6%
ValueCountFrequency (%)
20256059
80.1%
20241334
 
17.6%
202364
 
0.8%
202249
 
0.6%
202134
 
0.4%
20209
 
0.1%
20194
 
0.1%
20156
 
0.1%
20082
 
< 0.1%
20061
 
< 0.1%

Interactions

2025-10-09T10:38:52.897742image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
2025-10-09T10:38:50.435422image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
2025-10-09T10:38:51.307574image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
2025-10-09T10:38:51.805290image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
2025-10-09T10:38:52.338784image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
2025-10-09T10:38:53.008044image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
2025-10-09T10:38:50.538151image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
2025-10-09T10:38:51.413784image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
2025-10-09T10:38:51.916274image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
2025-10-09T10:38:52.458840image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
2025-10-09T10:38:53.148826image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
2025-10-09T10:38:50.643675image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
2025-10-09T10:38:51.504482image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
2025-10-09T10:38:52.014135image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
2025-10-09T10:38:52.563406image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
2025-10-09T10:38:53.300810image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
2025-10-09T10:38:50.753196image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
2025-10-09T10:38:51.601501image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
2025-10-09T10:38:52.122203image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
2025-10-09T10:38:52.673819image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
2025-10-09T10:38:53.472741image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
2025-10-09T10:38:51.205051image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
2025-10-09T10:38:51.704357image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
2025-10-09T10:38:52.233488image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
2025-10-09T10:38:52.782807image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/

Correlations

2025-10-09T10:39:03.531264image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
IMD_DecileIMD_Rankcountycurrent_energy_ratingdistrictladnmnew_buildpriceproperty_typetenuretotal_floor_areayear
IMD_Decile1.0000.9950.1630.0560.2190.2200.1040.4490.2100.0620.2080.034
IMD_Rank0.9951.0000.1630.0560.2190.2200.1070.4530.2100.0660.2110.036
county0.1630.1631.0000.0660.9980.9950.1070.0070.1540.1240.0480.018
current_energy_rating0.0560.0560.0661.0000.1070.1070.5450.0000.1470.1870.0520.042
district0.2190.2190.9980.1071.0000.9960.1780.0000.1910.1960.0720.000
ladnm0.2200.2200.9950.1070.9961.0000.1770.0000.1910.1960.0720.000
new_build0.1040.1070.1070.5450.1780.1771.0000.0000.1410.0000.0460.169
price0.4490.4530.0070.0000.0000.0000.0001.0000.0020.0000.696-0.010
property_type0.2100.2100.1540.1470.1910.1910.1410.0021.0000.8590.2230.049
tenure0.0620.0660.1240.1870.1960.1960.0000.0000.8591.0000.1040.075
total_floor_area0.2080.2110.0480.0520.0720.0720.0460.6960.2230.1041.000-0.026
year0.0340.0360.0180.0420.0000.0000.169-0.0100.0490.075-0.0261.000

Missing values

2025-10-09T10:38:53.673385image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
A simple visualization of nullity by column.
2025-10-09T10:38:53.884727image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-10-09T10:38:54.088074image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

transactionpricetransfer_datepostcodeproperty_typenew_buildtenurePAONSAONStreetLocalitytown_citydistrictcountylsoa11cdmsoa11nmladnmIMD_DecileIMD_Rankaddr_keycurrent_energy_ratingtotal_floor_areayear
110{3DCCB7C9-D19E-5B9D-E063-4704A8C0331E}790001996-11-29S18 1QHDNF27NaNHOLBEIN CLOSENaNDRONFIELDNORTH EAST DERBYSHIREDERBYSHIREE01019793North East Derbyshire 006North East Derbyshire10.031035.027 holbein close s18 1qhD156.021996
2{3DCCB7CA-8C58-5B9D-E063-4704A8C0331E}3500002001-07-19CV4 7PADNF1NaNTHE LAURELSNaNCOVENTRYCOVENTRYWEST MIDLANDSE01009665Coventry 042Coventry9.029510.01 the laurels cv4 7paC354.002001
58{3DCCB7CA-8C30-5B9D-E063-4704A8C0331E}1920002005-03-14B44 0JRSNF56NaNHORNSEY ROADNaNBIRMINGHAMBIRMINGHAMWEST MIDLANDSE01009128Birmingham 011Birmingham1.0433.056 hornsey road b44 0jrE76.872005
59{3DCCB7CA-8CCC-5B9D-E063-4704A8C0331E}1700002005-05-20DY4 7NYSYF22NaNDARBYS WAYNaNTIPTONSANDWELLWEST MIDLANDSE01009976Sandwell 016Sandwell7.021008.022 darbys way dy4 7nyD102.002005
1{3DCCB7C9-D364-5B9D-E063-4704A8C0331E}4500002006-03-17DE7 6GUDNF3NaNBEECH LANEWEST HALLAMILKESTONEREWASHDERBYSHIREE01019703Erewash 005Erewash10.029988.03 beech lane de7 6guD57.002006
172{3DCCB7CA-8D25-5B9D-E063-4704A8C0331E}1249502008-06-11DY8 3UJTNF76NaNSOUTH ROADNaNSTOURBRIDGEDUDLEYWEST MIDLANDSE01009848Dudley 038Dudley6.017668.076 south road dy8 3ujE60.432008
171{3DCCB7CA-1CF3-5B9D-E063-4704A8C0331E}2700002008-06-25LE5 5JPDNF55NaNROWSLEY STREETNaNLEICESTERLEICESTERLEICESTERE01013762Leicester 022Leicester3.07304.055 rowsley street le5 5jpC34.402008
98{3DCCB7CA-8AEF-5B9D-E063-4704A8C0331E}2850002015-03-28B31 2SQSNF12NaNKEMSHEAD AVENUENaNBIRMINGHAMBIRMINGHAMWEST MIDLANDSE01009162Birmingham 124Birmingham3.08482.012 kemshead avenue b31 2sqD78.002015
100{3DCCB7CA-8E32-5B9D-E063-4704A8C0331E}2825002015-07-18B97 6AXSNF26NaNMARTON CLOSENaNREDDITCHREDDITCHWORCESTERSHIREE01032226Redditch 001Redditch8.024851.026 marton close b97 6axB95.002015
72{3DCCB7CA-4BEC-5B9D-E063-4704A8C0331E}3225002015-07-23ST5 3AFDNF25NaNCLAYTON ROADNaNNEWCASTLENEWCASTLE-UNDER-LYMESTAFFORDSHIREE01029605Newcastle-under-Lyme 014Newcastle-under-Lyme9.027636.025 clayton road st5 3afD182.882015
transactionpricetransfer_datepostcodeproperty_typenew_buildtenurePAONSAONStreetLocalitytown_citydistrictcountylsoa11cdmsoa11nmladnmIMD_DecileIMD_Rankaddr_keycurrent_energy_ratingtotal_floor_areayear
3720{3DCCB7CA-88A9-5B9D-E063-4704A8C0331E}885002025-08-27WS3 4AQTNF182NaNWOLVERHAMPTON ROADPELSALLWALSALLWALSALLWEST MIDLANDSE01010350Walsall 003Walsall6.019134.0182 wolverhampton road ws3 4aqC87.02025
2609{3DCCB7CA-881E-5B9D-E063-4704A8C0331E}1200002025-08-27WS1 3PSTNF25NaNBRACE STREETNaNWALSALLWALSALLWEST MIDLANDSE01010371Walsall 030Walsall1.01053.025 brace street ws1 3psC89.02025
4699{3DCCB7CA-8985-5B9D-E063-4704A8C0331E}2500002025-08-27CV3 6DJTNF47NaNGREGORY AVENUENaNCOVENTRYCOVENTRYWEST MIDLANDSE01009666Coventry 041Coventry10.030010.047 gregory avenue cv3 6djC84.02025
6084{3DCCB7CA-4C17-5B9D-E063-4704A8C0331E}4850002025-08-27TF9 2QLDNF2NaNWOODPECKER VIEWLOGGERHEADSMARKET DRAYTONNEWCASTLE-UNDER-LYMESTAFFORDSHIREE01029572Newcastle-under-Lyme 016Newcastle-under-Lyme10.030493.02 woodpecker view tf9 2qlD196.02025
3914{3DCCB7CA-8B4F-5B9D-E063-4704A8C0331E}2250002025-08-28B26 3DJTNF53NaNBICKLEY GROVENaNBIRMINGHAMBIRMINGHAMWEST MIDLANDSE01009315Birmingham 081Birmingham2.05991.053 bickley grove b26 3djD77.02025
5712{3DCCB7CA-2668-5B9D-E063-4704A8C0331E}2080002025-08-28B13 8JTFNL52AFLAT 6SALISBURY ROADMOSELEYBIRMINGHAMBIRMINGHAMWEST MIDLANDSE01009184Birmingham 092Birmingham3.08032.052a flat 6 salisbury road b13 8jtD78.02025
4563{3DCCB7CA-8BF1-5B9D-E063-4704A8C0331E}1700002025-08-28B13 9PSFNL14NaNAVON DRIVEMOSELEYBIRMINGHAMBIRMINGHAMWEST MIDLANDSE01009185Birmingham 093Birmingham5.013722.014 avon drive b13 9psE72.02025
1594{3DCCB7C9-D2EC-5B9D-E063-4704A8C0331E}3200002025-08-28NG10 1PPDNF8NaNTEWKESBURY ROADLONG EATONNOTTINGHAMEREWASHDERBYSHIREE01019678Erewash 014Erewash9.029253.08 tewkesbury road ng10 1ppC97.02025
4336{3DCCB7CA-8930-5B9D-E063-4704A8C0331E}1700002025-08-28B36 9TZTNF2NaNPIKEHORNE CROFTNaNBIRMINGHAMSOLIHULLWEST MIDLANDSE01010120Solihull 001Solihull4.010097.02 pikehorne croft b36 9tzD66.02025
6680{3DCCB7CA-4D3F-5B9D-E063-4704A8C0331E}2500002025-08-29ST13 8XFDNF3NaNTHE WILLOWSNaNLEEKSTAFFORDSHIRE MOORLANDSSTAFFORDSHIREE01029816Staffordshire Moorlands 006Staffordshire Moorlands9.028838.03 the willows st13 8xfD56.02025